Global Lighthouse Network 2026

How Lighthouses adapt AI oversight to decision-making contexts FIGURE 22 HGIH (irreversible damage) WOL (simple, r epetitive)MUIDEM (patter ns exist)HIGH (ambiguous) MUIDEM (moderate consequence) WOL (minimal impact)Risk from a bad decision outcome Wuhu, China Shanghai, ChinaChangzhou, China Yancheng, China Wuhan, China Monterrey, MexicoTo addr ess fr equent or der delays caused by supply chain disruptions, a digital twin-based contr ol tower integrates data fr om 50+ sensors (e.g. internal systems, public news) and 40+ risk types (e.g. natural disasters, regulatory changes). Users r eceive r eal-time risk mitigation r ecommendations from a GenAI chatbot power ed by LLMs. Surge in new pr oducts and market expansion incr eased new pr oduct introduction (NPI) complexity , requiring br oader technical competencies and longer lead times (3-year ramp-up). AI platform with 21 agents automates 50% of tasks (e.g. data collection, document generation), enabling teams to focus on pr ocess optimization and machine design. To impr ove maintenance efficiency and r educe downtime, AI-based self- diagnosis platform collects sensor and machine data every 5 seconds. CNN algorithms detect faults and trigger alerts, while LLMs pr ovide actionable advice, such as adjusting pallet jack speed and optimizing maintenance plans. LSTM models pr edict r emaining part life with pr ecision, enabling pr oactive maintenance and stabilizing machine performance.Digital twin- enabled supply chain contr ol tower Technical competency for NPI acceleration AI agents AI-based self- diagnostic equipment management Floor scales pr ovide millions of configurable variants and engineer ed-to-or der (ETO) solutions, with 66% one-piece or ders pr ocessed daily . Traditional rigid production lines hinder delivery agility . Through multi-system integration, discr ete event simulation (DES) and genetic algorithm (GA) modular cluster workstations ar e dynamically r econfigur ed via r eal-time scheduling, r esolving complex line-balancing and constraints.Simulation-based cluster workstations reconfiguration Manual tuning of injection moulding parameters led to inefficiencies, quality issues and energy waste. Multi-objective optimization model with dynamic compensation and analysis of r eal-time data fr om 500+ parameters identified top factors. This appr oach, leveraging historical data and advanced algorithms, enabled intelligent r ecommendations and self- adaptive optimization.Multi-objective optimization for injection moulding Managing 100k+ daily small batch or ders acr oss many contr ol points resulted in slow r oot cause analysis. Site-built E2E supply chain contr ol tower integrates 10+ systems, pr oviding r eal-time visibility acr oss all or der nodes for intelligent alerts. A 13-layer decision tr ee model with AIGC automates r oot cause analysis and generates optimal solutions for fast r esolution.AI-enabled E2E exception-driven supply chain contr ols With 5,000+ SKUs and manual setup of 70+ parameters on 60-year -old machines, operations faced challenges in yield, lead time and defect rate. ML models trained on 2+ years of golden batch data now pr escribe optimal settings, dir ectly transferr ed to machine PLCs. A continuous ML pipeline r etrains models using outcomes and operator feedback, impr oving quality and efficiency .First-time-right production enabled by IIoT and ML1 2 3 4 5 6 Shirwal, Indi a7-85% emit dael redr O -67% emit dael IP N -58% tsoc ecnanetnia M +13% Manufacturing on-time delivery -15% emit elcy C -39% emit dael yrevile D +37% dleiy ssap tsri FLighthouse examplesAI as a collaborator Recommends options, human validates1AI as an advisor Provides insights, human decides3Human only Strong AI gover nance r equir ed 2Al as an assistant Supports tasks, no decisionsConditional autonomy Executes tasks, human r eviews exceptions4Al override possible AI-assisted advanced parameter setting 6 Independent Al Acts independently with no human oversight neededAl as an executor Makes decisions under close monitoring5Al as an operator Runs operations, human oversees 7 Complexity of a decision Notes: AIGC = artificial intelligence-generated content, CNN = convolutional neural network, LSTM = long short-term memory. Source: Global Lighthouse Network. Global Lighthouse Network: Rewiring Operations for Resilience and Impact at Scale 35